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      Dispersion of SARS-CoV-2 lineage BA.5.1.25 and its descendants in Peru during two COVID-19 waves in 2022

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          Abstract

          During the third year of the pandemic in Peru, the persistent transmission of SARS-CoV-2 led to the appearance of more transmissible and immune-evasive Omicron sublineages; in that context, the National Genomic Surveillance of SARS-CoV-2 performed by the Peruvian National Institute of Health detected spike mutations in the circulating Omicron BA.5.1.25 sublineage which was later designated as DJ.1 and increased during the fourth COVID-19 wave, this eventually branched into new sublineages. The introduction, emergence, and timing of the most recent common ancestor (tMRCA) of BA.5.1.25 and its descendants (DJ.1, DJ.1.1, DJ.1.2, and DJ.1.3) were investigated in this paper as well as the time lags between their emergence and identification by the Peruvian National Institute of Health. Our findings show that ongoing genomic surveillance of SARS-CoV-2 is critical for understanding its phylogenetic evolution and the emergence of novel variations.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              Fast and accurate long-read alignment with Burrows–Wheeler transform

              Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                nataliavh84@gmail.com
                Journal
                Genomics Inform
                Genomics Inform
                Genomics & Informatics
                BioMed Central (London )
                1598-866X
                2234-0742
                31 May 2024
                31 May 2024
                2024
                : 22
                : 5
                Affiliations
                Centro Nacional de Salud Pública, Instituto Nacional de Salud, ( https://ror.org/03gx6zj11) Capac Yupanqui 1400-Jesus Maria, Lima, Peru
                Author information
                http://orcid.org/0000-0001-6547-6999
                http://orcid.org/0000-0002-0948-0114
                http://orcid.org/0000-0001-8776-8888
                http://orcid.org/0000-0003-3667-6806
                http://orcid.org/0000-0002-0562-0431
                http://orcid.org/0000-0002-3740-1962
                Article
                6
                10.1186/s44342-024-00006-3
                11184951
                38907313
                8c83a54c-40ba-4010-98f8-ce7e1201a480
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 July 2023
                : 4 March 2024
                Categories
                Research
                Custom metadata
                © Korea Genome Organization 2024

                Genetics
                genomics,surveillance,covid-19,sars-cov-2,omicron,peru
                Genetics
                genomics, surveillance, covid-19, sars-cov-2, omicron, peru

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